Valohai utilizes Docker images to define your runtime environment. This means that the platform is capable of running any code from C to Python as long as it can run inside a Docker container.
You can use any Docker image available online. After getting initial versions working, it makes sense to package your dependencies by building your own images.
We recommend hosting your images on Docker Hub as it’s the most straight forward but you can use any Docker registry. You can configure authenticated access under organization settings.
Here are the most common Docker images currently used on the platform:
tensorflow/tensorflow:<VERSION>-gpu # e.g. 2.6.1-gpu, for GPU support tensorflow/tensorflow:<VERSION> # e.g. 2.6.1, for CPU only pytorchlightning/pytorch_lightning:latest-py<version>-torch<version> # e.g. py3.6-torch1.6 pytorch/pytorch:<VERSION> # e.g. 1.3-cuda10.1-cudnn7-runtime python:<VERSION> # e.g. 3.8.0 r-base:<VERSION> # e.g. 3.6.1 julia:<VERSION> # e.g. 1.3.0 valohai/fbprophet # Valohai hosted image with Prophet valohai/sklearn:1.0 # Valohai hosted image with sklean valohai/xgboost:1.4.2 # Valohai hosted image with xgboost
Which images to use depend on your specific use-case, but it usually makes sense to:
start with as minimal image as possible
use a specific image tag (the
:<VERSION>part) so everything stays reproducible